本文關(guān)鍵詞：沈陽市中心城區(qū)交通網(wǎng)絡(luò)中心性及其與第三產(chǎn)業(yè)經(jīng)濟密度空間分布的關(guān)系，由筆耕文化傳播整理發(fā)布。

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摘要：

多中心性評價模型(Multiple Centrality Analysis，即MCA)可用于分析交通網(wǎng)絡(luò)中心性及其與城市經(jīng)濟活動的關(guān)系，其所包含的鄰近度、介數(shù)中心性及直達性是測度城市土地開發(fā)利用率的重要指標(biāo)。本文首先測度沈陽市中心城區(qū)交通網(wǎng)絡(luò)中心性；通過核密度估計法對交通網(wǎng)絡(luò)中心性與第三產(chǎn)業(yè)經(jīng)濟密度進行空間插值，將兩者轉(zhuǎn)換為同一計算單位，測算兩者相關(guān)系數(shù)，分析第三產(chǎn)業(yè)經(jīng)濟密度空間分布與交通網(wǎng)絡(luò)中心性的空間關(guān)系及其統(tǒng)計學(xué)特征；其中第三產(chǎn)業(yè)經(jīng)濟密度為面域數(shù)據(jù)，需在ArcGIS 中建立漁網(wǎng)進行空間插值。研究結(jié)果如下：① 交通網(wǎng)絡(luò)中心性對第三產(chǎn)業(yè)經(jīng)濟密度空間具有決定性影響，交通網(wǎng)絡(luò)的多中心性導(dǎo)致了經(jīng)濟活動的多中心性；② 第三產(chǎn)業(yè)經(jīng)濟密度空間分布受介數(shù)中心性影響最大，直達性對第三產(chǎn)業(yè)經(jīng)濟密度空間分布影響也較大，而鄰近度對第三產(chǎn)業(yè)經(jīng)濟密度分布影響較小。研究有助于整體把握沈陽市中心城區(qū)交通網(wǎng)絡(luò)中心性空間分布狀態(tài)，為城市經(jīng)濟活動布局提供科學(xué)依據(jù)，在城市規(guī)劃理論與實踐研究中具有指導(dǎo)意義。

關(guān)鍵詞： 經(jīng)濟密度   多中心性評價模型   沈陽市   第三產(chǎn)業(yè)   交通網(wǎng)絡(luò)中心性  

基金：

國家自然科學(xué)基金項目(41071109)。

School of Geographical Science, Northeast Normal University, Changchun 130024, China  

Abstract：

With the development of network science, many scholars abroad begin to focus on the research of centrality of traffic network based on MCA(Multiple Centrality Assessment) and its relationship with economic activities. Centrality of traffic network is calibrated in a MCA model composed of multiple measures such as closeness, betweenness, and straightness. MCA model is a very important indicator that measures the rate of land development and utilization, and is widely used both in the theoretical and empirical inquiries. In this paper, by using the tools developed by MIT to calculate centrality of traffic network and its relationship with economic activities precisely and efficiently, we investigated the geography of three centralities of traffic network and their correlations with economic density of tertiary industry in Shenyang City, and then applied the KDE method to both centralities of traffic network and economic density to examine the correlations between them. Since economic density is regional data based on subdistricts, we created fishnet in ArcGIS and then did spatial interpolation. The results indicated that centralities of traffic network are correlated with the spatial distribution of economic density of tertiary industry in Shenyang. Spatial distribution of economic activity density correlates highly with the betweenness of traffic network, which means that the multiple centers of the streets lead to multiple centralities of economic activities. But we found that only betweenness and straightness show clear multi-centricity. Closeness, however, just has single centrality. This also means closeness has less impact on economic activities than betweenness and straightness. The major contributions made by this research can be summarized as follows: (1) Improving overall understanding of the spatial distribution of street centralities in Shenyang, which can be one of the most powerful determinants for urban planners and designers to understand how a city works and to decide where renovation and redevelopment need to be placed, to guide economic layout. (2) The concept that central urban arterials should be conceived as the cores, not the borders, of neighborhoods has the importance of directing in the theory and practice of city planning. (3) By drawing lessons from foreign research experiences, this research can enrich the theory, methods, and practice of the street network centrality in our country. If we take into account the relative properties of different street grades and types, vehicle flow rate and capacity, one-way or two-way streets, and so on, and give them appropriate weights based on their properties, the results will be more actual and practical and can help us understand the centrality of traffic network and its relationship with economic activities more precisely. More works need be done in order to study centrality of traffic network and its relationship with economic activities more comprehensively: (1) By looking into centrality of traffic network and its relationship with every kind of economic activity, we can get clear dependent relationship between centrality of traffic network and economic activities profoundly, and better understand the different relationships between them. (2) If we can get different attributes of every traffic level and use them as weights when we do KDE analysis, the research results will be much more practical.

Keywords： centrality of traffic network   Mmultiple Centrality Assessment Model   tertiary industry   economic density   Shenyang City  

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  本文關(guān)鍵詞：沈陽市中心城區(qū)交通網(wǎng)絡(luò)中心性及其與第三產(chǎn)業(yè)經(jīng)濟密度空間分布的關(guān)系，，由筆耕文化傳播整理發(fā)布。